Overview

Dataset statistics

Number of variables16
Number of observations2697
Missing cells201
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory337.2 KiB
Average record size in memory128.0 B

Variable types

Numeric4
Text7
Categorical4
Boolean1

Alerts

decision_type is highly imbalanced (76.3%)Imbalance
disposition has 59 (2.2%) missing valuesMissing
issue_area has 113 (4.2%) missing valuesMissing
Unnamed: 0 has unique valuesUnique
ID has unique valuesUnique
href has unique valuesUnique
facts has unique valuesUnique

Reproduction

Analysis started2024-02-17 04:38:57.904443
Analysis finished2024-02-17 04:39:08.611095
Duration10.71 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIQUE 

Distinct2697
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1653.9941
Minimum0
Maximum3302
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-02-16T22:39:08.732091image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile155.8
Q1809
median1662
Q32487
95-th percentile3140.4
Maximum3302
Range3302
Interquartile range (IQR)1678

Descriptive statistics

Standard deviation962.68494
Coefficient of variation (CV)0.58203651
Kurtosis-1.2185905
Mean1653.9941
Median Absolute Deviation (MAD)840
Skewness-0.012126281
Sum4460822
Variance926762.29
MonotonicityNot monotonic
2024-02-16T22:39:08.918099image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
471 1
 
< 0.1%
2918 1
 
< 0.1%
3154 1
 
< 0.1%
1392 1
 
< 0.1%
3045 1
 
< 0.1%
1780 1
 
< 0.1%
3210 1
 
< 0.1%
1986 1
 
< 0.1%
704 1
 
< 0.1%
1062 1
 
< 0.1%
Other values (2687) 2687
99.6%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
3302 1
< 0.1%
3301 1
< 0.1%
3300 1
< 0.1%
3299 1
< 0.1%
3298 1
< 0.1%
3297 1
< 0.1%
3296 1
< 0.1%
3295 1
< 0.1%
3294 1
< 0.1%
3293 1
< 0.1%

ID
Real number (ℝ)

UNIQUE 

Distinct2697
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56355.736
Minimum50606
Maximum63335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-02-16T22:39:09.128288image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum50606
5-th percentile51487.4
Q154289
median55271
Q359478
95-th percentile63013.4
Maximum63335
Range12729
Interquartile range (IQR)5189

Descriptive statistics

Standard deviation3640.0948
Coefficient of variation (CV)0.06459138
Kurtosis-0.69010176
Mean56355.736
Median Absolute Deviation (MAD)1108
Skewness0.74086293
Sum1.5199142 × 108
Variance13250290
MonotonicityNot monotonic
2024-02-16T22:39:09.313273image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53057 1
 
< 0.1%
62582 1
 
< 0.1%
63029 1
 
< 0.1%
54983 1
 
< 0.1%
62821 1
 
< 0.1%
55400 1
 
< 0.1%
63143 1
 
< 0.1%
55613 1
 
< 0.1%
53925 1
 
< 0.1%
54628 1
 
< 0.1%
Other values (2687) 2687
99.6%
ValueCountFrequency (%)
50606 1
< 0.1%
50613 1
< 0.1%
50623 1
< 0.1%
50632 1
< 0.1%
50643 1
< 0.1%
50644 1
< 0.1%
50655 1
< 0.1%
50657 1
< 0.1%
50663 1
< 0.1%
50671 1
< 0.1%
ValueCountFrequency (%)
63335 1
< 0.1%
63332 1
< 0.1%
63331 1
< 0.1%
63324 1
< 0.1%
63323 1
< 0.1%
63322 1
< 0.1%
63321 1
< 0.1%
63319 1
< 0.1%
63318 1
< 0.1%
63316 1
< 0.1%

name
Text

Distinct2643
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2024-02-16T22:39:09.611108image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length148
Median length92
Mean length31.418242
Min length10

Characters and Unicode

Total characters84735
Distinct characters78
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2601 ?
Unique (%)96.4%

Sample

1st rowCity of Oklahoma City v. Tuttle
2nd rowCity of Ontario v. Quon
3rd rowCity of Philadelphia v. New Jersey
4th rowCity of Philadelphia v. New Jersey
5th rowCity of Rancho Palos Verdes v. Abrams
ValueCountFrequency (%)
v 2690
 
19.9%
united 542
 
4.0%
states 524
 
3.9%
of 417
 
3.1%
inc 380
 
2.8%
company 139
 
1.0%
corporation 81
 
0.6%
80
 
0.6%
new 77
 
0.6%
city 69
 
0.5%
Other values (3889) 8492
62.9%
2024-02-16T22:39:10.269310image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10804
 
12.8%
e 6425
 
7.6%
n 5499
 
6.5%
a 5384
 
6.4%
o 5086
 
6.0%
i 4978
 
5.9%
t 4906
 
5.8%
r 4165
 
4.9%
. 3503
 
4.1%
s 3498
 
4.1%
Other values (68) 30487
36.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 58950
69.6%
Space Separator 10804
 
12.8%
Uppercase Letter 10681
 
12.6%
Other Punctuation 4108
 
4.8%
Dash Punctuation 121
 
0.1%
Decimal Number 53
 
0.1%
Open Punctuation 8
 
< 0.1%
Close Punctuation 8
 
< 0.1%
Final Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6425
10.9%
n 5499
9.3%
a 5384
9.1%
o 5086
 
8.6%
i 4978
 
8.4%
t 4906
 
8.3%
r 4165
 
7.1%
s 3498
 
5.9%
v 3122
 
5.3%
l 2816
 
4.8%
Other values (21) 13071
22.2%
Uppercase Letter
ValueCountFrequency (%)
S 1341
 
12.6%
C 1328
 
12.4%
M 680
 
6.4%
U 659
 
6.2%
I 655
 
6.1%
A 511
 
4.8%
L 474
 
4.4%
P 473
 
4.4%
R 444
 
4.2%
D 424
 
4.0%
Other values (16) 3692
34.6%
Decimal Number
ValueCountFrequency (%)
1 17
32.1%
9 6
 
11.3%
5 6
 
11.3%
7 6
 
11.3%
3 4
 
7.5%
2 4
 
7.5%
0 3
 
5.7%
4 3
 
5.7%
8 3
 
5.7%
6 1
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 3503
85.3%
, 478
 
11.6%
& 83
 
2.0%
' 42
 
1.0%
/ 1
 
< 0.1%
# 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
10804
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 121
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Final Punctuation
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 69631
82.2%
Common 15104
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6425
 
9.2%
n 5499
 
7.9%
a 5384
 
7.7%
o 5086
 
7.3%
i 4978
 
7.1%
t 4906
 
7.0%
r 4165
 
6.0%
s 3498
 
5.0%
v 3122
 
4.5%
l 2816
 
4.0%
Other values (47) 23752
34.1%
Common
ValueCountFrequency (%)
10804
71.5%
. 3503
 
23.2%
, 478
 
3.2%
- 121
 
0.8%
& 83
 
0.5%
' 42
 
0.3%
1 17
 
0.1%
( 8
 
0.1%
) 8
 
0.1%
9 6
 
< 0.1%
Other values (11) 34
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84726
> 99.9%
None 7
 
< 0.1%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10804
 
12.8%
e 6425
 
7.6%
n 5499
 
6.5%
a 5384
 
6.4%
o 5086
 
6.0%
i 4978
 
5.9%
t 4906
 
5.8%
r 4165
 
4.9%
. 3503
 
4.1%
s 3498
 
4.1%
Other values (62) 30478
36.0%
None
ValueCountFrequency (%)
é 2
28.6%
í 2
28.6%
ó 1
14.3%
ã 1
14.3%
ñ 1
14.3%
Punctuation
ValueCountFrequency (%)
2
100.0%

href
Text

UNIQUE 

Distinct2697
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2024-02-16T22:39:10.487304image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length45
Median length44
Mean length38.137931
Min length33

Characters and Unicode

Total characters102858
Distinct characters30
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2697 ?
Unique (%)100.0%

Sample

1st rowhttps://api.oyez.org/cases/1984/83-1919
2nd rowhttps://api.oyez.org/cases/2009/08-1332
3rd rowhttps://api.oyez.org/cases/1976/75-1150
4th rowhttps://api.oyez.org/cases/1977/77-404
5th rowhttps://api.oyez.org/cases/2004/03-1601
ValueCountFrequency (%)
https://api.oyez.org/cases/1984/83-1919 1
 
< 0.1%
https://api.oyez.org/cases/2004/03-1669 1
 
< 0.1%
https://api.oyez.org/cases/2011/11-1053 1
 
< 0.1%
https://api.oyez.org/cases/2017/16-424 1
 
< 0.1%
https://api.oyez.org/cases/1976/75-1150 1
 
< 0.1%
https://api.oyez.org/cases/1977/77-404 1
 
< 0.1%
https://api.oyez.org/cases/2004/03-1601 1
 
< 0.1%
https://api.oyez.org/cases/1985/84-1360 1
 
< 0.1%
https://api.oyez.org/cases/1988/87-998 1
 
< 0.1%
https://api.oyez.org/cases/1985/85-224 1
 
< 0.1%
Other values (2687) 2687
99.6%
2024-02-16T22:39:10.879292image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 13485
 
13.1%
s 8142
 
7.9%
o 5415
 
5.3%
a 5395
 
5.2%
p 5394
 
5.2%
. 5394
 
5.2%
e 5394
 
5.2%
t 5394
 
5.2%
1 4885
 
4.7%
0 3907
 
3.8%
Other values (20) 40053
38.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 54133
52.6%
Decimal Number 24718
24.0%
Other Punctuation 21576
 
21.0%
Dash Punctuation 2419
 
2.4%
Connector Punctuation 12
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 8142
15.0%
o 5415
10.0%
a 5395
10.0%
p 5394
10.0%
e 5394
10.0%
t 5394
10.0%
i 2721
 
5.0%
r 2718
 
5.0%
g 2718
 
5.0%
c 2700
 
5.0%
Other values (5) 8142
15.0%
Decimal Number
ValueCountFrequency (%)
1 4885
19.8%
0 3907
15.8%
9 3629
14.7%
2 2708
11.0%
8 1927
 
7.8%
7 1865
 
7.5%
5 1603
 
6.5%
6 1554
 
6.3%
4 1336
 
5.4%
3 1304
 
5.3%
Other Punctuation
ValueCountFrequency (%)
/ 13485
62.5%
. 5394
 
25.0%
: 2697
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 2419
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 54133
52.6%
Common 48725
47.4%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 13485
27.7%
. 5394
 
11.1%
1 4885
 
10.0%
0 3907
 
8.0%
9 3629
 
7.4%
2 2708
 
5.6%
: 2697
 
5.5%
- 2419
 
5.0%
8 1927
 
4.0%
7 1865
 
3.8%
Other values (5) 5809
11.9%
Latin
ValueCountFrequency (%)
s 8142
15.0%
o 5415
10.0%
a 5395
10.0%
p 5394
10.0%
e 5394
10.0%
t 5394
10.0%
i 2721
 
5.0%
r 2718
 
5.0%
g 2718
 
5.0%
c 2700
 
5.0%
Other values (5) 8142
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 102858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 13485
 
13.1%
s 8142
 
7.9%
o 5415
 
5.3%
a 5395
 
5.2%
p 5394
 
5.2%
. 5394
 
5.2%
e 5394
 
5.2%
t 5394
 
5.2%
1 4885
 
4.7%
0 3907
 
3.8%
Other values (20) 40053
38.9%

docket
Text

Distinct2574
Distinct (%)95.8%
Missing10
Missing (%)0.4%
Memory size21.2 KiB
2024-02-16T22:39:11.174502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.9854857
Min length1

Characters and Unicode

Total characters16083
Distinct characters23
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2514 ?
Unique (%)93.6%

Sample

1st row83-1919
2nd row08-1332
3rd row75-1150
4th row77-404
5th row03-1601
ValueCountFrequency (%)
2 6
 
0.2%
6 6
 
0.2%
13 6
 
0.2%
8 6
 
0.2%
5 6
 
0.2%
23 5
 
0.2%
9 5
 
0.2%
7 5
 
0.2%
1 4
 
0.1%
3 4
 
0.1%
Other values (2564) 2637
98.0%
2024-02-16T22:39:11.671613image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2564
15.9%
- 2379
14.8%
0 1556
9.7%
9 1473
9.2%
8 1281
8.0%
7 1273
7.9%
5 1185
7.4%
2 1104
6.9%
4 1065
6.6%
6 1064
6.6%
Other values (13) 1139
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13593
84.5%
Dash Punctuation 2379
 
14.8%
Lowercase Letter 94
 
0.6%
Uppercase Letter 14
 
0.1%
Space Separator 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2564
18.9%
0 1556
11.4%
9 1473
10.8%
8 1281
9.4%
7 1273
9.4%
5 1185
8.7%
2 1104
8.1%
4 1065
7.8%
6 1064
7.8%
3 1028
7.6%
Lowercase Letter
ValueCountFrequency (%)
g 21
22.3%
r 21
22.3%
i 21
22.3%
o 21
22.3%
u 5
 
5.3%
s 5
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
M 3
21.4%
I 3
21.4%
S 3
21.4%
C 3
21.4%
A 2
14.3%
Dash Punctuation
ValueCountFrequency (%)
- 2379
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15975
99.3%
Latin 108
 
0.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2564
16.1%
- 2379
14.9%
0 1556
9.7%
9 1473
9.2%
8 1281
8.0%
7 1273
8.0%
5 1185
7.4%
2 1104
6.9%
4 1065
6.7%
6 1064
6.7%
Other values (2) 1031
6.5%
Latin
ValueCountFrequency (%)
g 21
19.4%
r 21
19.4%
i 21
19.4%
o 21
19.4%
u 5
 
4.6%
s 5
 
4.6%
M 3
 
2.8%
I 3
 
2.8%
S 3
 
2.8%
C 3
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16083
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2564
15.9%
- 2379
14.8%
0 1556
9.7%
9 1473
9.2%
8 1281
8.0%
7 1273
7.9%
5 1185
7.4%
2 1104
6.9%
4 1065
6.6%
6 1064
6.6%
Other values (13) 1139
7.1%

term
Text

Distinct70
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2024-02-16T22:39:11.898606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length9
Median length4
Mean length4.0908417
Min length4

Characters and Unicode

Total characters11033
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1984
2nd row2009
3rd row1976
4th row1977
5th row2004
ValueCountFrequency (%)
2015 73
 
2.7%
2005 72
 
2.7%
2011 71
 
2.6%
2002 70
 
2.6%
2009 70
 
2.6%
1997 69
 
2.6%
1996 69
 
2.6%
2004 68
 
2.5%
1999 68
 
2.5%
1998 68
 
2.5%
Other values (60) 1999
74.1%
2024-02-16T22:39:12.295799image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2342
21.2%
1 2296
20.8%
9 2150
19.5%
2 1587
14.4%
8 636
 
5.8%
7 584
 
5.3%
6 485
 
4.4%
5 410
 
3.7%
4 257
 
2.3%
3 237
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10984
99.6%
Dash Punctuation 49
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2342
21.3%
1 2296
20.9%
9 2150
19.6%
2 1587
14.4%
8 636
 
5.8%
7 584
 
5.3%
6 485
 
4.4%
5 410
 
3.7%
4 257
 
2.3%
3 237
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2342
21.2%
1 2296
20.8%
9 2150
19.5%
2 1587
14.4%
8 636
 
5.8%
7 584
 
5.3%
6 485
 
4.4%
5 410
 
3.7%
4 257
 
2.3%
3 237
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2342
21.2%
1 2296
20.8%
9 2150
19.5%
2 1587
14.4%
8 636
 
5.8%
7 584
 
5.3%
6 485
 
4.4%
5 410
 
3.7%
4 257
 
2.3%
3 237
 
2.1%
Distinct2222
Distinct (%)82.4%
Missing1
Missing (%)< 0.1%
Memory size21.2 KiB
2024-02-16T22:39:12.553946image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length223
Median length107
Mean length22.247033
Min length1

Characters and Unicode

Total characters59978
Distinct characters79
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2091 ?
Unique (%)77.6%

Sample

1st rowCity of Oklahoma City
2nd rowCity of Ontario, California et al.
3rd rowCity of Philadelphia et al.
4th rowCity of Philadelphia
5th rowCity of Rancho Palos Verdes, California, et al.
ValueCountFrequency (%)
et 417
 
4.5%
al 401
 
4.3%
of 378
 
4.1%
united 247
 
2.7%
states 238
 
2.6%
inc 208
 
2.3%
and 122
 
1.3%
company 88
 
1.0%
the 76
 
0.8%
corporation 62
 
0.7%
Other values (3068) 7006
75.8%
2024-02-16T22:39:13.110109image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6569
 
11.0%
e 5375
 
9.0%
a 4425
 
7.4%
n 3941
 
6.6%
t 3752
 
6.3%
o 3700
 
6.2%
r 3437
 
5.7%
i 3344
 
5.6%
l 2498
 
4.2%
s 2289
 
3.8%
Other values (69) 20648
34.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42942
71.6%
Uppercase Letter 8065
 
13.4%
Space Separator 6569
 
11.0%
Other Punctuation 2289
 
3.8%
Dash Punctuation 63
 
0.1%
Decimal Number 41
 
0.1%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Control 2
 
< 0.1%
Final Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5375
12.5%
a 4425
10.3%
n 3941
9.2%
t 3752
8.7%
o 3700
8.6%
r 3437
 
8.0%
i 3344
 
7.8%
l 2498
 
5.8%
s 2289
 
5.3%
c 1415
 
3.3%
Other values (18) 8766
20.4%
Uppercase Letter
ValueCountFrequency (%)
S 893
 
11.1%
C 852
 
10.6%
M 545
 
6.8%
D 426
 
5.3%
A 423
 
5.2%
R 411
 
5.1%
L 410
 
5.1%
I 401
 
5.0%
P 351
 
4.4%
J 343
 
4.3%
Other values (16) 3010
37.3%
Decimal Number
ValueCountFrequency (%)
1 8
19.5%
5 6
14.6%
9 4
9.8%
7 4
9.8%
2 4
9.8%
0 4
9.8%
3 4
9.8%
8 3
 
7.3%
6 2
 
4.9%
4 2
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 1223
53.4%
, 989
43.2%
& 40
 
1.7%
' 22
 
1.0%
; 7
 
0.3%
/ 3
 
0.1%
" 2
 
0.1%
# 2
 
0.1%
: 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
6569
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 63
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Control
ValueCountFrequency (%)
2
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 51007
85.0%
Common 8971
 
15.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5375
 
10.5%
a 4425
 
8.7%
n 3941
 
7.7%
t 3752
 
7.4%
o 3700
 
7.3%
r 3437
 
6.7%
i 3344
 
6.6%
l 2498
 
4.9%
s 2289
 
4.5%
c 1415
 
2.8%
Other values (44) 16831
33.0%
Common
ValueCountFrequency (%)
6569
73.2%
. 1223
 
13.6%
, 989
 
11.0%
- 63
 
0.7%
& 40
 
0.4%
' 22
 
0.2%
1 8
 
0.1%
; 7
 
0.1%
5 6
 
0.1%
9 4
 
< 0.1%
Other values (15) 40
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59975
> 99.9%
None 2
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6569
 
11.0%
e 5375
 
9.0%
a 4425
 
7.4%
n 3941
 
6.6%
t 3752
 
6.3%
o 3700
 
6.2%
r 3437
 
5.7%
i 3344
 
5.6%
l 2498
 
4.2%
s 2289
 
3.8%
Other values (66) 20645
34.4%
None
ValueCountFrequency (%)
é 1
50.0%
á 1
50.0%
Punctuation
ValueCountFrequency (%)
1
100.0%
Distinct2121
Distinct (%)78.7%
Missing1
Missing (%)< 0.1%
Memory size21.2 KiB
2024-02-16T22:39:13.329102image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length193
Median length109
Mean length22.735905
Min length1

Characters and Unicode

Total characters61296
Distinct characters74
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1996 ?
Unique (%)74.0%

Sample

1st rowRose Marie Tuttle, Individually and as Administratrix of the Estate of Tuttle
2nd rowJeff Quon, et al.
3rd rowNew Jersey et al.
4th rowNew Jersey
5th rowMark J. Abrams
ValueCountFrequency (%)
et 518
 
5.4%
al 508
 
5.3%
of 449
 
4.7%
united 322
 
3.4%
states 312
 
3.3%
inc 206
 
2.2%
and 106
 
1.1%
the 84
 
0.9%
company 75
 
0.8%
state 70
 
0.7%
Other values (2952) 6855
72.1%
2024-02-16T22:39:13.766301image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6819
 
11.1%
e 5400
 
8.8%
a 4747
 
7.7%
t 4094
 
6.7%
n 3918
 
6.4%
i 3746
 
6.1%
o 3714
 
6.1%
r 3283
 
5.4%
l 2615
 
4.3%
s 2436
 
4.0%
Other values (64) 20524
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 44008
71.8%
Uppercase Letter 7925
 
12.9%
Space Separator 6819
 
11.1%
Other Punctuation 2411
 
3.9%
Dash Punctuation 69
 
0.1%
Decimal Number 45
 
0.1%
Open Punctuation 9
 
< 0.1%
Close Punctuation 9
 
< 0.1%
Final Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5400
12.3%
a 4747
10.8%
t 4094
9.3%
n 3918
8.9%
i 3746
8.5%
o 3714
8.4%
r 3283
 
7.5%
l 2615
 
5.9%
s 2436
 
5.5%
c 1358
 
3.1%
Other values (17) 8697
19.8%
Uppercase Letter
ValueCountFrequency (%)
C 942
 
11.9%
S 901
 
11.4%
A 511
 
6.4%
M 446
 
5.6%
I 425
 
5.4%
U 385
 
4.9%
D 380
 
4.8%
B 368
 
4.6%
R 363
 
4.6%
L 340
 
4.3%
Other values (16) 2864
36.1%
Decimal Number
ValueCountFrequency (%)
1 18
40.0%
0 6
 
13.3%
3 4
 
8.9%
5 4
 
8.9%
7 4
 
8.9%
2 3
 
6.7%
9 2
 
4.4%
4 2
 
4.4%
8 1
 
2.2%
6 1
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 1274
52.8%
, 1043
43.3%
& 46
 
1.9%
' 26
 
1.1%
; 20
 
0.8%
/ 2
 
0.1%
Space Separator
ValueCountFrequency (%)
6819
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 69
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 51933
84.7%
Common 9363
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5400
 
10.4%
a 4747
 
9.1%
t 4094
 
7.9%
n 3918
 
7.5%
i 3746
 
7.2%
o 3714
 
7.2%
r 3283
 
6.3%
l 2615
 
5.0%
s 2436
 
4.7%
c 1358
 
2.6%
Other values (43) 16622
32.0%
Common
ValueCountFrequency (%)
6819
72.8%
. 1274
 
13.6%
, 1043
 
11.1%
- 69
 
0.7%
& 46
 
0.5%
' 26
 
0.3%
; 20
 
0.2%
1 18
 
0.2%
( 9
 
0.1%
) 9
 
0.1%
Other values (11) 30
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61294
> 99.9%
Punctuation 1
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6819
 
11.1%
e 5400
 
8.8%
a 4747
 
7.7%
t 4094
 
6.7%
n 3918
 
6.4%
i 3746
 
6.1%
o 3714
 
6.1%
r 3283
 
5.4%
l 2615
 
4.3%
s 2436
 
4.0%
Other values (62) 20522
33.5%
Punctuation
ValueCountFrequency (%)
1
100.0%
None
ValueCountFrequency (%)
ñ 1
100.0%

facts
Text

UNIQUE 

Distinct2697
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2024-02-16T22:39:14.006303image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length5858
Median length1512
Mean length1108.5821
Min length26

Characters and Unicode

Total characters2989846
Distinct characters104
Distinct categories16 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2697 ?
Unique (%)100.0%

Sample

1st row<p>On October 10, 1980, an Oklahoma City police officer shot and killed Albert Tuttle outside a bar. Rose Marie Tuttle, Albert’s widow, sued the police officer and the city in district court under Section 1983 of the Civil Rights Act of 1871, which allows an individual to recover damages against a party who “acting under color of state law” deprives another of his constitutional rights. The district court instructed the jury that the city could be held liable only if the incident had been caused by a municipal “policy,” but a single, unusually excessive use of force could support a finding that the city was grossly negligent or deliberately indifferent in the training or supervision of its police force and was therefore liable under Section 1983. The jury returned a verdict in favor of the police officer but against the city and awarded Tuttle’s estate $1.5 million in damages. The U.S. Court of Appeals for the Tenth Circuit affirmed.</p>
2nd row<p>Employees of the City of Ontario, California police department filed a 42 U.S.C. § 1983 claim in a California federal district court against the police department, city, chief of police, and an internal affairs officer. They alleged Fourth Amendment violations in relation to the police department's review of text messages made by an employee on a city issued text-message pager. While the city did not have an official text-messaging privacy policy, it did have a general "Computer Usage, Internet and E-mail Policy." The policy in part stated that "[t]he City of Ontario reserves the right to monitor and log all network activity including e-mail and Internet use, with or without notice," and that "[u]sers should have no expectation of privacy or confidentiality when using these resources." Employees were told verbally that the text-messaging pagers were considered e-mail and subject to the general policy. The district court entered judgment in favor of the defendants.</p> <p>On appeal, the U.S. Court of Appeals for the Ninth Circuit reversed in part. The court held that city employees had a reasonable expectation of privacy for the text messages they sent on their city-issued pagers because there was no text message privacy policy in place. Moreover, the court noted that the police department's review of the text messages was unreasonable because it could have used "less intrusive methods" to determine whether employees' had properly used the text messaging service.</p>
3rd row<p>A New Jersey statute prohibited the importation of solid or liquid waste into the state, except for garbage for swine feed. The City of Philadelphia challenged the statute, alleging it was unconstitutional under the Commerce clause of Article I and pre-empted by the Solid Waste Disposal Act of 1965. The New Jersey Supreme Court upheld the statute. Congress then passed the Resource Conservation and Recovery Act of 1976.</p>
4th row<p>A New Jersey law prohibited the importation of most "solid or liquid waste which originated or was collected outside the territorial limits of the State."</p>
5th row<p>Rancho Palos Verdes, a city in California, gave Mark Abrams a permit to construct an antenna on his property for amateur use. But when the city learned Abrams used the antenna for commercial purposes, the city forced Abrams to stop until he got a commercial use permit. Abrams applied and the city refused to give him the permit. Abrams then sued in federal district court, alleging the city violated his rights under the Telecommunications Act of 1996. Abrams sought damages under a federal liability law that allowed people to sue for damages for federal rights violations.</p> <p>The district court agreed with Abrams and ordered the city to give Abrams the permit. But the court refused Abrams' request for damages under the separate federal liability law. The court said Congress intended for violations of rights under the Telecommunications Act to include only remedies specifically found in that act. The Ninth Circuit Court of Appeals reversed and ruled that because the act did not contain a "comprehensive remedial scheme," Abrams could seek damages under other federal laws.</p>
ValueCountFrequency (%)
the 40890
 
8.8%
of 16316
 
3.5%
to 12754
 
2.8%
and 11396
 
2.5%
a 10687
 
2.3%
that 9621
 
2.1%
court 8325
 
1.8%
in 8247
 
1.8%
for 6017
 
1.3%
was 5414
 
1.2%
Other values (25883) 333638
72.0%
2024-02-16T22:39:14.466512image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
459702
15.4%
e 293885
 
9.8%
t 235741
 
7.9%
a 184692
 
6.2%
i 181559
 
6.1%
n 164827
 
5.5%
o 164608
 
5.5%
r 158578
 
5.3%
s 135876
 
4.5%
d 105090
 
3.5%
Other values (94) 905288
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2307428
77.2%
Space Separator 459912
 
15.4%
Uppercase Letter 100304
 
3.4%
Other Punctuation 62612
 
2.1%
Decimal Number 22520
 
0.8%
Math Symbol 19626
 
0.7%
Control 4280
 
0.1%
Dash Punctuation 3826
 
0.1%
Close Punctuation 2796
 
0.1%
Open Punctuation 2760
 
0.1%
Other values (6) 3782
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 293885
12.7%
t 235741
10.2%
a 184692
 
8.0%
i 181559
 
7.9%
n 164827
 
7.1%
o 164608
 
7.1%
r 158578
 
6.9%
s 135876
 
5.9%
d 105090
 
4.6%
h 104892
 
4.5%
Other values (21) 577680
25.0%
Uppercase Letter
ValueCountFrequency (%)
C 14658
14.6%
A 12053
12.0%
S 10346
 
10.3%
T 10103
 
10.1%
I 4822
 
4.8%
M 4567
 
4.6%
D 4303
 
4.3%
F 4192
 
4.2%
P 3649
 
3.6%
U 3585
 
3.6%
Other values (16) 28026
27.9%
Other Punctuation
ValueCountFrequency (%)
. 26555
42.4%
, 21747
34.7%
/ 5269
 
8.4%
' 4254
 
6.8%
" 3759
 
6.0%
; 416
 
0.7%
§ 193
 
0.3%
& 161
 
0.3%
: 142
 
0.2%
% 74
 
0.1%
Other values (3) 42
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 4785
21.2%
0 3755
16.7%
9 3264
14.5%
2 2799
12.4%
5 1501
 
6.7%
8 1424
 
6.3%
6 1256
 
5.6%
7 1250
 
5.6%
3 1245
 
5.5%
4 1241
 
5.5%
Math Symbol
ValueCountFrequency (%)
> 9757
49.7%
< 9757
49.7%
= 103
 
0.5%
8
 
< 0.1%
+ 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 3705
96.8%
92
 
2.4%
27
 
0.7%
2
 
0.1%
Space Separator
ValueCountFrequency (%)
459702
> 99.9%
  210
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 2657
95.0%
] 139
 
5.0%
Open Punctuation
ValueCountFrequency (%)
( 2621
95.0%
[ 139
 
5.0%
Final Punctuation
ValueCountFrequency (%)
1853
72.4%
708
 
27.6%
Initial Punctuation
ValueCountFrequency (%)
711
96.5%
26
 
3.5%
Control
ValueCountFrequency (%)
4280
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 413
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 67
100.0%
Line Separator
ValueCountFrequency (%)
2
100.0%
Other Number
ValueCountFrequency (%)
½ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2407732
80.5%
Common 582114
 
19.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 293885
12.2%
t 235741
 
9.8%
a 184692
 
7.7%
i 181559
 
7.5%
n 164827
 
6.8%
o 164608
 
6.8%
r 158578
 
6.6%
s 135876
 
5.6%
d 105090
 
4.4%
h 104892
 
4.4%
Other values (47) 677984
28.2%
Common
ValueCountFrequency (%)
459702
79.0%
. 26555
 
4.6%
, 21747
 
3.7%
> 9757
 
1.7%
< 9757
 
1.7%
/ 5269
 
0.9%
1 4785
 
0.8%
4280
 
0.7%
' 4254
 
0.7%
" 3759
 
0.6%
Other values (37) 32249
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2985979
99.9%
Punctuation 3437
 
0.1%
None 422
 
< 0.1%
Misc Technical 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
459702
15.4%
e 293885
 
9.8%
t 235741
 
7.9%
a 184692
 
6.2%
i 181559
 
6.1%
n 164827
 
5.5%
o 164608
 
5.5%
r 158578
 
5.3%
s 135876
 
4.6%
d 105090
 
3.5%
Other values (76) 901421
30.2%
Punctuation
ValueCountFrequency (%)
1853
53.9%
711
 
20.7%
708
 
20.6%
92
 
2.7%
27
 
0.8%
26
 
0.8%
16
 
0.5%
2
 
0.1%
2
 
0.1%
None
ValueCountFrequency (%)
  210
49.8%
§ 193
45.7%
á 9
 
2.1%
ó 3
 
0.7%
é 2
 
0.5%
½ 2
 
0.5%
í 2
 
0.5%
ü 1
 
0.2%
Misc Technical
ValueCountFrequency (%)
8
100.0%

facts_len
Real number (ℝ)

Distinct1444
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1108.5821
Minimum26
Maximum5858
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-02-16T22:39:14.727720image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile375.8
Q1746
median1051
Q31382
95-th percentile2076.4
Maximum5858
Range5832
Interquartile range (IQR)636

Descriptive statistics

Standard deviation527.23266
Coefficient of variation (CV)0.47559188
Kurtosis3.9144131
Mean1108.5821
Median Absolute Deviation (MAD)317
Skewness1.1538625
Sum2989846
Variance277974.28
MonotonicityNot monotonic
2024-02-16T22:39:14.928712image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1174 8
 
0.3%
986 7
 
0.3%
954 7
 
0.3%
1415 7
 
0.3%
1280 7
 
0.3%
1118 6
 
0.2%
1067 6
 
0.2%
1290 6
 
0.2%
780 6
 
0.2%
847 6
 
0.2%
Other values (1434) 2631
97.6%
ValueCountFrequency (%)
26 1
< 0.1%
30 1
< 0.1%
31 1
< 0.1%
105 1
< 0.1%
106 1
< 0.1%
107 1
< 0.1%
125 1
< 0.1%
145 1
< 0.1%
147 1
< 0.1%
162 1
< 0.1%
ValueCountFrequency (%)
5858 1
< 0.1%
4299 1
< 0.1%
3538 1
< 0.1%
3526 1
< 0.1%
3516 1
< 0.1%
3281 1
< 0.1%
3248 1
< 0.1%
3215 1
< 0.1%
3206 1
< 0.1%
3174 1
< 0.1%

majority_vote
Real number (ℝ)

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.028921
Minimum0
Maximum9
Zeros19
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-02-16T22:39:15.074927image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median7
Q39
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.7184787
Coefficient of variation (CV)0.24448685
Kurtosis0.1180519
Mean7.028921
Median Absolute Deviation (MAD)2
Skewness-0.47051477
Sum18957
Variance2.9531692
MonotonicityNot monotonic
2024-02-16T22:39:15.211910image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
9 849
31.5%
5 651
24.1%
6 453
16.8%
7 353
13.1%
8 346
12.8%
4 26
 
1.0%
0 19
 
0.7%
ValueCountFrequency (%)
0 19
 
0.7%
4 26
 
1.0%
5 651
24.1%
6 453
16.8%
7 353
13.1%
8 346
12.8%
9 849
31.5%
ValueCountFrequency (%)
9 849
31.5%
8 346
12.8%
7 353
13.1%
6 453
16.8%
5 651
24.1%
4 26
 
1.0%
0 19
 
0.7%

minority_vote
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
0
1048 
4
570 
3
443 
2
374 
1
262 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2697
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row4
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0 1048
38.9%
4 570
21.1%
3 443
16.4%
2 374
 
13.9%
1 262
 
9.7%

Length

2024-02-16T22:39:15.349905image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T22:39:15.483900image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1048
38.9%
4 570
21.1%
3 443
16.4%
2 374
 
13.9%
1 262
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 1048
38.9%
4 570
21.1%
3 443
16.4%
2 374
 
13.9%
1 262
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2697
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1048
38.9%
4 570
21.1%
3 443
16.4%
2 374
 
13.9%
1 262
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
Common 2697
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1048
38.9%
4 570
21.1%
3 443
16.4%
2 374
 
13.9%
1 262
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2697
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1048
38.9%
4 570
21.1%
3 443
16.4%
2 374
 
13.9%
1 262
 
9.7%
Distinct2
Distinct (%)0.1%
Missing12
Missing (%)0.4%
Memory size21.2 KiB
True
1764 
False
921 
(Missing)
 
12
ValueCountFrequency (%)
True 1764
65.4%
False 921
34.1%
(Missing) 12
 
0.4%
2024-02-16T22:39:15.608822image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

decision_type
Categorical

IMBALANCE 

Distinct10
Distinct (%)0.4%
Missing5
Missing (%)0.2%
Memory size21.2 KiB
majority opinion
2320 
per curiam
 
216
plurality opinion
 
123
equally divided
 
12
dismissal - rule 46
 
6
Other values (5)
 
15

Length

Max length33
Median length16
Mean length15.599183
Min length10

Characters and Unicode

Total characters41993
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowplurality opinion
2nd rowmajority opinion
3rd rowper curiam
4th rowmajority opinion
5th rowmajority opinion

Common Values

ValueCountFrequency (%)
majority opinion 2320
86.0%
per curiam 216
 
8.0%
plurality opinion 123
 
4.6%
equally divided 12
 
0.4%
dismissal - rule 46 6
 
0.2%
dismissal - improvidently granted 5
 
0.2%
dismissal - other 5
 
0.2%
dismissal - moot 3
 
0.1%
memorandum 1
 
< 0.1%
opinion of the court 1
 
< 0.1%
(Missing) 5
 
0.2%

Length

2024-02-16T22:39:15.769812image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T22:39:15.915799image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
opinion 2444
45.1%
majority 2320
42.8%
per 216
 
4.0%
curiam 216
 
4.0%
plurality 123
 
2.3%
dismissal 19
 
0.4%
19
 
0.4%
equally 12
 
0.2%
divided 12
 
0.2%
46 6
 
0.1%
Other values (9) 28
 
0.5%

Most occurring characters

ValueCountFrequency (%)
i 7619
18.1%
o 7227
17.2%
n 4899
11.7%
r 2898
 
6.9%
p 2788
 
6.6%
2723
 
6.5%
a 2696
 
6.4%
m 2566
 
6.1%
t 2463
 
5.9%
y 2460
 
5.9%
Other values (15) 3654
8.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39239
93.4%
Space Separator 2723
 
6.5%
Dash Punctuation 19
 
< 0.1%
Decimal Number 12
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 7619
19.4%
o 7227
18.4%
n 4899
12.5%
r 2898
 
7.4%
p 2788
 
7.1%
a 2696
 
6.9%
m 2566
 
6.5%
t 2463
 
6.3%
y 2460
 
6.3%
j 2320
 
5.9%
Other values (11) 1303
 
3.3%
Decimal Number
ValueCountFrequency (%)
4 6
50.0%
6 6
50.0%
Space Separator
ValueCountFrequency (%)
2723
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 39239
93.4%
Common 2754
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 7619
19.4%
o 7227
18.4%
n 4899
12.5%
r 2898
 
7.4%
p 2788
 
7.1%
a 2696
 
6.9%
m 2566
 
6.5%
t 2463
 
6.3%
y 2460
 
6.3%
j 2320
 
5.9%
Other values (11) 1303
 
3.3%
Common
ValueCountFrequency (%)
2723
98.9%
- 19
 
0.7%
4 6
 
0.2%
6 6
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41993
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 7619
18.1%
o 7227
17.2%
n 4899
11.7%
r 2898
 
6.9%
p 2788
 
6.6%
2723
 
6.5%
a 2696
 
6.4%
m 2566
 
6.1%
t 2463
 
5.9%
y 2460
 
5.9%
Other values (15) 3654
8.7%

disposition
Categorical

MISSING 

Distinct9
Distinct (%)0.3%
Missing59
Missing (%)2.2%
Memory size21.2 KiB
reversed/remanded
885 
affirmed
809 
reversed
479 
vacated/remanded
360 
reversed in-part/remanded
 
49
Other values (4)
 
56

Length

Max length25
Median length24
Mean length12.448825
Min length4

Characters and Unicode

Total characters32840
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowreversed
2nd rowreversed/remanded
3rd rowvacated/remanded
4th rowreversed
5th rowreversed/remanded

Common Values

ValueCountFrequency (%)
reversed/remanded 885
32.8%
affirmed 809
30.0%
reversed 479
17.8%
vacated/remanded 360
13.3%
reversed in-part/remanded 49
 
1.8%
none 30
 
1.1%
reversed in-part 17
 
0.6%
vacated 6
 
0.2%
vacated in-part/remanded 3
 
0.1%
(Missing) 59
 
2.2%

Length

2024-02-16T22:39:16.141052image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T22:39:16.287046image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
reversed/remanded 885
32.7%
affirmed 809
29.9%
reversed 545
20.1%
vacated/remanded 360
13.3%
in-part/remanded 52
 
1.9%
none 30
 
1.1%
in-part 17
 
0.6%
vacated 9
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 8092
24.6%
d 5202
15.8%
r 5035
15.3%
a 2913
 
8.9%
m 2106
 
6.4%
v 1799
 
5.5%
f 1618
 
4.9%
s 1430
 
4.4%
n 1426
 
4.3%
/ 1297
 
3.9%
Other values (7) 1922
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31405
95.6%
Other Punctuation 1297
 
3.9%
Space Separator 69
 
0.2%
Dash Punctuation 69
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8092
25.8%
d 5202
16.6%
r 5035
16.0%
a 2913
 
9.3%
m 2106
 
6.7%
v 1799
 
5.7%
f 1618
 
5.2%
s 1430
 
4.6%
n 1426
 
4.5%
i 878
 
2.8%
Other values (4) 906
 
2.9%
Other Punctuation
ValueCountFrequency (%)
/ 1297
100.0%
Space Separator
ValueCountFrequency (%)
69
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 69
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31405
95.6%
Common 1435
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8092
25.8%
d 5202
16.6%
r 5035
16.0%
a 2913
 
9.3%
m 2106
 
6.7%
v 1799
 
5.7%
f 1618
 
5.2%
s 1430
 
4.6%
n 1426
 
4.5%
i 878
 
2.8%
Other values (4) 906
 
2.9%
Common
ValueCountFrequency (%)
/ 1297
90.4%
69
 
4.8%
- 69
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8092
24.6%
d 5202
15.8%
r 5035
15.3%
a 2913
 
8.9%
m 2106
 
6.4%
v 1799
 
5.5%
f 1618
 
4.9%
s 1430
 
4.4%
n 1426
 
4.3%
/ 1297
 
3.9%
Other values (7) 1922
 
5.9%

issue_area
Categorical

MISSING 

Distinct14
Distinct (%)0.5%
Missing113
Missing (%)4.2%
Memory size21.2 KiB
Criminal Procedure
709 
Civil Rights
470 
Economic Activity
439 
First Amendment
290 
Judicial Power
274 
Other values (9)
402 

Length

Max length20
Median length17
Mean length14.754257
Min length6

Characters and Unicode

Total characters38125
Distinct characters32
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCivil Rights
2nd rowCriminal Procedure
3rd rowEconomic Activity
4th rowEconomic Activity
5th rowCivil Rights

Common Values

ValueCountFrequency (%)
Criminal Procedure 709
26.3%
Civil Rights 470
17.4%
Economic Activity 439
16.3%
First Amendment 290
10.8%
Judicial Power 274
 
10.2%
Due Process 102
 
3.8%
Federalism 96
 
3.6%
Privacy 59
 
2.2%
Unions 46
 
1.7%
Federal Taxation 45
 
1.7%
Other values (4) 54
 
2.0%
(Missing) 113
 
4.2%

Length

2024-02-16T22:39:16.520041image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
criminal 709
14.4%
procedure 709
14.4%
civil 470
9.6%
rights 470
9.6%
economic 439
8.9%
activity 439
8.9%
first 290
5.9%
amendment 290
5.9%
judicial 274
 
5.6%
power 274
 
5.6%
Other values (13) 552
11.2%

Most occurring characters

ValueCountFrequency (%)
i 5253
13.8%
r 3028
 
7.9%
e 2833
 
7.4%
c 2482
 
6.5%
2332
 
6.1%
o 2108
 
5.5%
t 2045
 
5.4%
n 1920
 
5.0%
m 1824
 
4.8%
l 1633
 
4.3%
Other values (22) 12667
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 30877
81.0%
Uppercase Letter 4916
 
12.9%
Space Separator 2332
 
6.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 5253
17.0%
r 3028
9.8%
e 2833
9.2%
c 2482
 
8.0%
o 2108
 
6.8%
t 2045
 
6.6%
n 1920
 
6.2%
m 1824
 
5.9%
l 1633
 
5.3%
d 1414
 
4.6%
Other values (9) 6337
20.5%
Uppercase Letter
ValueCountFrequency (%)
C 1179
24.0%
P 1146
23.3%
A 763
15.5%
R 471
 
9.6%
E 439
 
8.9%
F 431
 
8.8%
J 274
 
5.6%
D 102
 
2.1%
U 46
 
0.9%
T 45
 
0.9%
Other values (2) 20
 
0.4%
Space Separator
ValueCountFrequency (%)
2332
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 35793
93.9%
Common 2332
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 5253
14.7%
r 3028
 
8.5%
e 2833
 
7.9%
c 2482
 
6.9%
o 2108
 
5.9%
t 2045
 
5.7%
n 1920
 
5.4%
m 1824
 
5.1%
l 1633
 
4.6%
d 1414
 
4.0%
Other values (21) 11253
31.4%
Common
ValueCountFrequency (%)
2332
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38125
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 5253
13.8%
r 3028
 
7.9%
e 2833
 
7.4%
c 2482
 
6.5%
2332
 
6.1%
o 2108
 
5.5%
t 2045
 
5.4%
n 1920
 
5.0%
m 1824
 
4.8%
l 1633
 
4.3%
Other values (22) 12667
33.2%

Interactions

2024-02-16T22:39:07.427954image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T22:39:05.814797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T22:39:06.351774image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T22:39:06.902752image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T22:39:07.548103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T22:39:05.974793image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T22:39:06.481761image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T22:39:07.047952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T22:39:07.673090image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T22:39:06.107815image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T22:39:06.650757image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T22:39:07.176948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T22:39:07.799085image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T22:39:06.233785image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T22:39:06.781751image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-16T22:39:07.306953image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-02-16T22:39:07.974089image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-16T22:39:08.363322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0IDnamehrefdockettermfirst_partysecond_partyfactsfacts_lenmajority_voteminority_votefirst_party_winnerdecision_typedispositionissue_area
047153057City of Oklahoma City v. Tuttlehttps://api.oyez.org/cases/1984/83-191983-19191984City of Oklahoma CityRose Marie Tuttle, Individually and as Administratrix of the Estate of Tuttle<p>On October 10, 1980, an Oklahoma City police officer shot and killed Albert Tuttle outside a bar. Rose Marie Tuttle, Albert’s widow, sued the police officer and the city in district court under Section 1983 of the Civil Rights Act of 1871, which allows an individual to recover damages against a party who “acting under color of state law” deprives another of his constitutional rights. The district court instructed the jury that the city could be held liable only if the incident had been caused by a municipal “policy,” but a single, unusually excessive use of force could support a finding that the city was grossly negligent or deliberately indifferent in the training or supervision of its police force and was therefore liable under Section 1983. The jury returned a verdict in favor of the police officer but against the city and awarded Tuttle’s estate $1.5 million in damages. The U.S. Court of Appeals for the Tenth Circuit affirmed.</p>\n95271Trueplurality opinionreversedCivil Rights
1207155708City of Ontario v. Quonhttps://api.oyez.org/cases/2009/08-133208-13322009City of Ontario, California et al.Jeff Quon, et al.<p>Employees of the City of Ontario, California police department filed a 42 U.S.C. § 1983 claim in a California federal district court against the police department, city, chief of police, and an internal affairs officer. They alleged Fourth Amendment violations in relation to the police department's review of text messages made by an employee on a city issued text-message pager. While the city did not have an official text-messaging privacy policy, it did have a general "Computer Usage, Internet and E-mail Policy." The policy in part stated that "[t]he City of Ontario reserves the right to monitor and log all network activity including e-mail and Internet use, with or without notice," and that "[u]sers should have no expectation of privacy or confidentiality when using these resources." Employees were told verbally that the text-messaging pagers were considered e-mail and subject to the general policy. The district court entered judgment in favor of the defendants.</p>\n<p>On appeal, the U.S. Court of Appeals for the Ninth Circuit reversed in part. The court held that city employees had a reasonable expectation of privacy for the text messages they sent on their city-issued pagers because there was no text message privacy policy in place. Moreover, the court noted that the police department's review of the text messages was unreasonable because it could have used "less intrusive methods" to determine whether employees' had properly used the text messaging service.</p>\n149490Truemajority opinionreversed/remandedCriminal Procedure
216951555City of Philadelphia v. New Jerseyhttps://api.oyez.org/cases/1976/75-115075-11501976City of Philadelphia et al.New Jersey et al.<p>A New Jersey statute prohibited the importation of solid or liquid waste into the state, except for garbage for swine feed. The City of Philadelphia challenged the statute, alleging it was unconstitutional under the Commerce clause of Article I and pre-empted by the Solid Waste Disposal Act of 1965. The New Jersey Supreme Court upheld the statute. Congress then passed the Resource Conservation and Recovery Act of 1976.</p>\n43054Trueper curiamvacated/remandedEconomic Activity
323051844City of Philadelphia v. New Jerseyhttps://api.oyez.org/cases/1977/77-40477-4041977City of PhiladelphiaNew Jersey<p>A New Jersey law prohibited the importation of most "solid or liquid waste which originated or was collected outside the territorial limits of the State."</p>\n16272Truemajority opinionreversedEconomic Activity
4162655235City of Rancho Palos Verdes v. Abramshttps://api.oyez.org/cases/2004/03-160103-16012004City of Rancho Palos Verdes, California, et al.Mark J. Abrams<p>Rancho Palos Verdes, a city in California, gave Mark Abrams a permit to construct an antenna on his property for amateur use. But when the city learned Abrams used the antenna for commercial purposes, the city forced Abrams to stop until he got a commercial use permit. Abrams applied and the city refused to give him the permit. Abrams then sued in federal district court, alleging the city violated his rights under the Telecommunications Act of 1996. Abrams sought damages under a federal liability law that allowed people to sue for damages for federal rights violations.</p>\n<p>The district court agreed with Abrams and ordered the city to give Abrams the permit. But the court refused Abrams' request for damages under the separate federal liability law. The court said Congress intended for violations of rights under the Telecommunications Act to include only remedies specifically found in that act. The Ninth Circuit Court of Appeals reversed and ruled that because the act did not contain a "comprehensive remedial scheme," Abrams could seek damages under other federal laws.</p>\n109490Truemajority opinionreversed/remandedCivil Rights
547753099City of Renton v. Playtime Theatres, Inc.https://api.oyez.org/cases/1985/84-136084-13601985City of RentonPlaytime Theatres, Inc.<p>The city of Renton, Washington, enacted a zoning ordinance that prohibited adult motion picture theaters from locating with in 1,000 feet of "any residential zone, single-or multiple-family dwelling, church, park, or school." Playtime Theatres, Inc., challenged the ordinance and sought a permanent injunction against its enforcement.</p>\n34272Truemajority opinionreversedFirst Amendment
661953621City of Richmond v. J. A. Croson Companyhttps://api.oyez.org/cases/1988/87-99887-9981988City of RichmondJ. A. Croson Company<p>In 1983, the City Council of Richmond, Virginia adopted regulations that required companies awarded city construction contracts to subcontract 30 percent of their business to minority business enterprises. The J.A. Croson Company, which lost its contract because of the 30 percent set-aside, brought suit against the city.</p>\n33063Falsemajority opinionaffirmedCivil Rights
750053212City of Riverside v. Riverahttps://api.oyez.org/cases/1985/85-22485-2241985City of RiversideRivera<p>In 1975, eight Chicano individuals were attending a party that was broken up by the Riverside police using tear gas and physical force without a warrant. Subsequently, the eight individuals filed suit in Federal District Court against the city and various police officers under several federal Civil Rights Acts, alleging violations of their First, Fourth, and Fourteenth Amendment rights. The jury found in the individuals' favor and awarded $33,350 in compensatory and punitive damages. The individuals also sought attorney's fees under the Civil Rights Attorney's Fees Awards Act of 1976 in the amount of $245,456.25, based on 1,946.75 hours expended by their two attorneys at $125 per hour and 84.5 hours expended by law clerks at $25 per hour. Finding both the hours and rates reasonable, the District Court awarded the requested amount, and the Court of Appeals affirmed. The appellate court found that the fee award was not excessive merely because it exceeded the amount of damages awarded by the jury.</p>\n101854Falseplurality opinionaffirmedAttorneys
8162755236City of San Diego v. Roehttps://api.oyez.org/cases/2004/03-166903-16692004City of San Diego, CaliforniaJohn Roe<p>John Roe, a San Diego police officer, was fired for selling a video on eBay that showed him stripping off a police uniform and masturbating. He then sued the city in federal district court and alleged his firing violated his First Amendment right to freedom of speech. The district court ruled against the officer; the Ninth Circuit reversed.</p>\n35090Trueper curiamreversedFirst Amendment
9163955248City of Sherrill v. Oneida Indian Nation of New Yorkhttps://api.oyez.org/cases/2004/03-85503-8552004City of Sherrill, New YorkOneida Indian Nation of New York, et al.<p>In the late 18th century, Congress set aside most of the tribal land of the Oneida Indian Nation of New York as a reservation. The tribe later sold off much of the reservation. In the 1990s members of the tribe began to buy back pieces of the land. The tribe said the reacquired land was part of a reservation and therefore exempt from state and municipal taxes. The City of Sherrill - which encompassed some of the tribe's property - argued the land was not tax-exempt. The Oneidas sued Sherrill in federal district court and alleged the land was recognized by the 1794 Treaty of Canandaigua as part of their historic reservation. The Oneidas also pointed to the 1790 Non- Intercourse Act that required federal consent for Indian land to lose its reservation status. Sherrill argued the land lost its reservation status after leaving the Oneidas' ownership originally. The district court and the Second Circuit Court of Appeals ruled for the Oneidas.</p>\n95981Truemajority opinionreversed/remandedCivil Rights
Unnamed: 0IDnamehrefdockettermfirst_partysecond_partyfactsfacts_lenmajority_voteminority_votefirst_party_winnerdecision_typedispositionissue_area
2687136754953Zelman v. Simmons-Harrishttps://api.oyez.org/cases/2001/00-175100-17512001ZelmanSimmons-Harris<p>Ohio's Pilot Project Scholarship Program provides tuition aid in the form of vouchers for certain students in the Cleveland City School District to attend participating public or private schools of their parent's choosing. Both religious and nonreligious schools in the district may participate. Tuition aid is distributed to parents according to financial need, and where the aid is spent depends solely upon where parents choose to enroll their children. In the 1999-2000 school year 82 percent of the participating private schools had a religious affiliation and 96 percent of the students participating in the scholarship program were enrolled in religiously affiliated schools. Sixty percent of the students were from families at or below the poverty line. A group of Ohio taxpayers sought to enjoin the program on the ground that it violated the Establishment Clause. The District Court granted them summary judgment, and the Court of Appeals affirmed.</p>\n96654Truemajority opinionreversedFirst Amendment
2688279362139Zenith Radio Corporation v. Hazeltine Research, Inc.https://api.oyez.org/cases/1970/80801970Zenith Radio CorporationHazeltine Research, Inc.<p>After refusing to renew a patent licensing agreement, Zenith Radio Corp., a radio and television manufacturer, was sued by Hazeltine Research, Inc., for patent infringement in United States District Court for the Northern District of Illinois. Zenith counterclaimed, alleging anti-trust violations, misuse of patents, and a conspiracy to restrain trade in Canada, England, and Australia. Zenith asked for treble damages and injunctive relief. Zenith contended that Hazeltine's license forced them to pay for use of unpatented products and that Hazeltine had illegally conspired with foreign patent pools to prevent Zenith from expanding into those markets.</p>\n<p>Before trial, Zenith had stipulated that Hazeltine and its parent corporation were one entity for the purposes of litigation. The District Court entered judgment against Hazeltine and its parent corporation, awarding Zenith treble damages and injunctive relief. The Court of Appeals for the Seventh Circuit affirmed the damages award, but otherwise reversed the District Court's judgment. The Court of Appeals vacated all judgments against Hazeltine's parent corporation because Zenith's pretrial stipulation did not properly designate the parent corporation as a party to the litigation.</p>\n126090Truemajority opinionreversed/remandedEconomic Activity
268989254439Zicherman v. Korean Air Lines Companyhttps://api.oyez.org/cases/1995/94-136194-13611995ZichermanKorean Air Lines Company<p>In 1983, Korean Air Lines (KAL) Flight KE007, en route from Alaska to South Korea entered the airspace of the former Soviet Union and was shot down. All 269 people on board were killed, including Muriel Kole. Subsequently, Marjorie Zicherman and Muriel Mahalek, Kole's sister and mother sued KAL under Article 17 of the Warsaw Convention, which governs international air transportation. Zicherman and Mahalek were awarded loss-of-society damages. The Court of Appeals set aside the verdict, holding that general maritime law supplied the substantive compensatory damages law to be applied in an action under the Warsaw Convention and that, under such law, a plaintiff can recover for loss of society only if he was the decedent's dependent at the time of death. The appellate court found that Mahalek had not established dependent status and remanded the case for the District Court to determine whether Zicherman was a dependent of the decedent.</p>\n95490Falsemajority opinionreversed in-partEconomic Activity
2690300762759Ziglar v. Abbasihttps://api.oyez.org/cases/2016/15-135815-13582016James W. ZiglarAhmer Iqbal Abbasi, et. al.<p dir="ltr">The respondents in this case are a group of male, non-U.S. citizens, most of whom are Muslim of Middle Eastern origin who were detained after the September 11, 2001 attacks and treated as “of interest” in the government’s investigation of these events. In their original claims, the plaintiffs alleged that they were detained without notice of the charges against them or information about how they were determined to be “of interest,” that their access to counsel and the courts was interfered with, and that they were subjected to excessively harsh treatment during their detention. They also asserted that their race, ethnicity, and national origin played a determinative role in the decision to detain them. The plaintiffs sued a number of government officials and argued that the government used their status as non-citizens to detain them when the government’s real purpose was to investigate whether they were terrorists and that the conditions of their confinement violated their Constitutional rights to due process and equal protection. After a series of motions to dismiss, the district court dismissed the claims regarding the length of confinement but allowed the Constitutional claims to proceed. Both the plaintiffs and defendants appealed various aspects of that ruling. </p>\n<p>While that appeal was pending, some of the plaintiffs settled their claims against the government and the U.S. Supreme Court decided Ashcroft v. Iqbal, which held that a complaint must allege sufficient facts to be plausible on its face and to allow a court to draw the reasonable inference that the defendant is liable for the claimed conduct. Based on these events, the U.S. Court of Appeals for the Second Circuit dismissed the length of confinement claims but remanded the conditions of confinement claims and allowed the plaintiffs to amend their complaint. The appellate court again dismissed some of the claims and allowed others to proceed.</p>\n196142Trueplurality opinionreversed/remandedCivil Rights
2691217355824Zivotofsky v. Clintonhttps://api.oyez.org/cases/2011/10-69910-6992011M. B. Z., By His Parents and Guardians Ari Z. Zivotofsky, et ux.Hillary Rodham Clinton, Secretary of State<p>Menachem Binyamin Zivotofsky is a United States citizen born on October 17, 2002 in Jerusalem. In December 2002, Zivotofsky's mother filed an application for a Consular Report of Birth Abroad and a United States passport for petitioner, listing his place of birth as "Jerusalem, Israel." United States diplomatic officials informed petitioner's mother that State Department policy required them to record "Jerusalem" as petitioner's place of birth, which is how petitioner's place of birth appears in the documents he received.</p>\n<p>On his behalf, Zivotofsky's parents filed this suit against the Secretary of State seeking an order compelling the State Department to identify petitioner's place of birth as "Jerusalem, Israel" in the official documents. The United States District Court for the District of Columbia initially dismissed the complaint after concluding that petitioner lacked standing, and that the complaint raised a nonjusticiable political question. United States Court of Appeals for the D.C. Circuit reversed and remanded, concluding that petitioner had standing and that a more complete record was needed on the foreign policy implications of recording "Israel" as Zivotofsky's place of birth.</p>\n<p>On remand, the State Department explained, among other things, that in the present circumstances if "Israel" were to be recorded as the place of birth of a person born in Jerusalem, such "unilateral action" by the United States on one of the most sensitive issues in the negotiations between Israelis and Palestinians "would critically compromise" the United States' ability to help further the Middle East peace process. The district court again dismissed on political question grounds. The court of appeals affirmed, holding that Zivotofsky's claim is foreclosed because it raises a nonjusticiable political question.</p>\n185181Truemajority opinionvacated/remandedMiscellaneous
2692241556102Zivotofsky v. Kerryhttps://api.oyez.org/cases/2014/13-62813-6282014M. B. Z., By His Parents and Guardians, Ari Z. Zivotofsky, et ux.John Kerry, Secretary of State<p>In 2002, Manachem Zivotofsky was born in Jerusalem to parents who are United States citizens. Manachem's parents requested that the U.S. State Department record his place of birth on his passport as "Israel," in accordance with Section 214(d) of the Foreign Relations Authorization Act of 2003 (Act). The State Department refused and instead issued Manachem a passport that listed "Jerusalem" as his place of birth. His parents sued the Secretary of State on his behalf and sought the enforcement of Section 214(d). The district court dismissed the case on the grounds that it presented a non-justiciable political question. The U.S. Supreme Court, in <em>Zivotofsky v. Clinton</em>, reversed that holding and remanded the case. On remand, the district court held that Section 214(d) "impermissibly intereferes" with the President's exclusive power to recognize foreign states. The U.S. Court of Appeals for the District of Columbia Circuit affirmed and held that the section goes beyond the scope of Congress's passport power to affect United States foreign policy, which is a realm the Constitution reserves for the executive branch.</p>\n114363Falsemajority opinionaffirmedMiscellaneous
269375154076Zobrest v. Catalina Foothills School Districthttps://api.oyez.org/cases/1992/92-9492-941992ZobrestCatalina Foothills School District<p>James Zobrest was deaf since birth. He attended public school through the eighth grade where the local school board provided a sign-language interpreter. Zobrest's parents elected to send their son to a Roman Catholic high school and requested that the local school board continue to provide their son with a sign-language interpreter. The school board denied the request on constitutional grounds. The Zobrests then filed suit, alleging that the Individuals with Disabilities Education Act (IDEA) and the Free Exercise Clause of the First Amendment required the school district to provide the interpreter and that the Establishment Clause did not bar such relief. The District Court granted the school district summary judgment on the ground that the interpreter would act as a conduit for the child's religious inculcation, thereby promoting his religious development at government expense in violation of the Establishment Clause. The Court of Appeals affirmed.</p>\n97254Truemajority opinionreversedFirst Amendment
2694293962620Zubik v. Burwellhttps://api.oyez.org/cases/2015/14-141814-14182015David A. Zubik, et al.Sylvia Burwell, Secretary of Health and Human Services, et al.<p>In 2010, Congress passed the Affordable Care Act (ACA), which requires group health plans and health insurance issuers offering health plans to provide preventative care and screenings for women pursuant to the guidelines established by the Department of Health and Human Services (HHS). These guidelines include “approved contraceptive methods, sterilization procedures, and patient education and counseling for all women with reproductive capacity.” The regulations include an exemption from contraceptive coverage for the group health plan of a religious employer. The exemption does not mean that such services are not covered, but that they are not covered through a cost-sharing mechanism.</p>\n<p>The petitioners are religious organizations that argue that the contraceptive coverage mandate of the ACA violates the Religious Freedom Restoration Act (RFRA), which Congress enacted in 1993, because the mandate requires these organizations to “facilitate” the provision of insurance coverage for contraceptive services that they oppose on religious grounds. In several separate cases, the relevant district courts issued injunctions against the government, and the relevant Courts of Appeals reversed. The appellate courts held that the religious organizations were unable to show that the contraceptive mandate substantially burdened the exercise of their religious freedom.</p>\n138880Truemajority opinionvacated/remandedFirst Amendment
2695180755427Zuni Public School Dist. No. 89 v. Department of Educationhttps://api.oyez.org/cases/2006/05-150805-15082006Zuni Public School District No. 89 et al.United States Department of Education et al.<p>The Department of Education certified that the state of New Mexico equalizes educational expenditures among school districts. The certification of equalization allowed New Mexico to offset its funding of districts located on Indian Reservations by a proportion of the federal Impact Aid payments made to those districts. Zuni Public School District objected to the certification, arguing that the Department had not followed the statutory formula for determining that a state's expenditures are equalized. Outlier school districts falling above the 95th or below the 5th percentile in per-pupil expenditures were excluded from consideration when the Department determined equalization. The Department calculated these percentiles based on the total student population, but Zuni argued that 20 U.S.C. Section 7709 had repealed that policy.</p>\n<p>An administrative judge dismissed Zuni's complaint, and the Secretary of Education affirmed on the ground that the law was ambiguous. A divided panel of the U.S. Circuit Court of Appeals for the Tenth Circuit upheld the Secretary's decision as a valid interpretation of the statute. In a rehearing by the entire Circuit Court, the 12 judges split evenly, again upholding the ruling.</p>\n123654Falsemajority opinionaffirmedJudicial Power
269622451804Zurcher v. Stanford Dailyhttps://api.oyez.org/cases/1977/76-148476-14841977ZurcherStanford Daily<p>In 1971, officers of the Palo Alto, California, Police Department obtained a warrant to search the main office of The Stanford Daily, the student newspaper at the university. It was believed that The Stanford Daily had pictures of a violent clash between a group of protesters and the police; the pictures were needed to identify the assailants. The officers searched The Daily's photographic laboratories, filing cabinets, desks, and waste paper baskets, but no materials were removed from the office. This case was decided together with Bergna v. Stanford Daily, involving the district attorney and a deputy district attorney who participated in the obtaining of the search warrant.</p>\n69253Truemajority opinionreversedCriminal Procedure